Building Intelligence in the Automated Traffic Signal Performance Measures with Advanced Data Analytics Huang, Tingting Poddar, Subhadipto Aguilar, Cristopher Sharma, Anuj Sharma, Anuj Smaglik, Edward Kothuri, Sirisha Koonce, Peter
dc.contributor.department Civil, Construction and Environmental Engineering
dc.contributor.department Institute for Transportation 2019-11-27T15:39:24.000 2020-06-30T01:11:42Z 2020-06-30T01:11:42Z Mon Jan 01 00:00:00 UTC 2018 2018-04-02 2018-01-01
dc.description.abstract <p>Automated traffic signal performance measures (ATSPMs) are an effort to equip traffic signal controllers with high-resolution data-logging capabilities and utilize this data to generate performance measures. These measures allow practitioners to improve operations as well as to maintain and operate their systems in a safe and efficient manner. Although these measures have changed the way that operators manage their systems, several shortcomings of the tool, identified by talking with signal operators, are a lack of data quality control and the extent of resources required to properly use the tool for system-wide management. To address these shortcomings, intelligent traffic signal performance measurements (ITSPMs) are presented in this paper, using the concepts of machine learning, traffic flow theory, and data visualization to reduce the operator resources needed for overseeing data-driven traffic signal management systems. In applying these concepts, ITSPMs provide graphical tools to identify and remove logging errors and data from bad sensors, intelligently determine trends in demand, and address the question of whether or not coordination may be needed at an intersection. The focus of ATSPMs and ITSPMs on performance measures for multimodal users is identified as a pressing need for future research.</p>
dc.description.comments <p>This is a manuscript of a proceeding published as Huang, Tingting, Subhadipto Poddar, Cristopher Aguilar, Anuj Sharma, Edward Smaglik, Sirisha Kothuri, and Peter Koonce. "Building Intelligence in the Automated Traffic Signal Performance Measures with Advanced Data Analytics." No. 18-05800. 2018. Transportation Research Board 97th Annual Meeting, Washington, DC, January 7-11, 2018. Posted with permission.</p>
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dc.identifier archive/
dc.identifier.articleid 1080
dc.identifier.contextkey 11888340
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath ccee_conf/80
dc.language.iso en
dc.source.bitstream archive/|||Sat Jan 15 02:04:52 UTC 2022
dc.subject.disciplines Civil Engineering
dc.subject.disciplines Computer-Aided Engineering and Design
dc.subject.disciplines Transportation Engineering
dc.title Building Intelligence in the Automated Traffic Signal Performance Measures with Advanced Data Analytics
dc.type article
dc.type.genre conference
dspace.entity.type Publication
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relation.isOrgUnitOfPublication 0cffd73a-b46d-4816-85f3-0f6ab7d2beb8
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